How to Use Prompt Engineering for Style Transfer and Text Rewriting with Pre-trained Language Models

Style transfer and text rewriting are two important tasks in natural language processing (NLP) that aim to modify the content or the form of a given text while preserving its meaning or message. For example, style transfer can change the tone, mood, or sentiment of a text, while text rewriting can paraphrase, summarize, or simplify a text.

Prompt Engineering for Style Transfer and Text Rewriting

However, these tasks are challenging because they require not only understanding the semantics and pragmatics of the original text, but also generating fluent and coherent text that meets the desired criteria. Moreover, there is often a lack of parallel data or explicit labels for these tasks, which makes it difficult to train supervised models.?

Prompt engineering is a technique that leverages the power of large-scale pre-trained language models (PLMs) such as GPT-3 or BERT to perform various NLP tasks without fine-tuning or additional training. Prompt engineering involves designing effective input and output formats, as well as choosing appropriate parameters and hyperparameters, to elicit the desired behavior from the PLMs.?

In today's newsletter edition, we will explore how prompt engineering can be used for style transfer and text rewriting tasks. We will also discuss some of the benefits and limitations of this approach, as well as some best practices and tips for prompt engineering.?

How Prompt Engineering Works for Style Transfer and Text Rewriting

Prompt engineering is based on the idea that PLMs have learned a lot of general and specific knowledge about language and the world from their large-scale pre-training on diverse and massive text corpora. Therefore, by providing them with carefully crafted inputs and outputs, we can induce them to perform various NLP tasks that they were not explicitly trained for.?For style transfer and text rewriting tasks, prompt engineering typically involves the following steps:?

1. Define the task and the desired output format.?

For example, for style transfer, we may want to change the sentiment of a text from negative to positive, or from formal to informal. For text rewriting, we may want to paraphrase, summarize, or simplify a text. The output format can be a single sentence, a paragraph, or a bullet point list.

2. Choose a suitable PLM and a generation method.?

Depending on the task and the output format, we may choose different PLMs and generation methods. For example, for style transfer, we may use GPT-3 with its default sampling method. For text rewriting, we may use BERT with masked language modeling (MLM) or GPT-3 with beam search.

3. Design an input prompt that provides enough context and guidance for the PLM to perform the task.?

The input prompt can include various elements such as keywords, examples, instructions, constraints, or templates. The input prompt should be clear, concise, and consistent with the output format.

4. Evaluate the output and refine the prompt if needed.?

The output should be checked for fluency, coherence, relevance, and accuracy. If the output is not satisfactory, we can try to modify the input prompt by adding or removing elements, changing the wording or the order, or adjusting the parameters or hyperparameters.

Benefits and Limitations of Prompt Engineering for Style Transfer and Text Rewriting

Prompt engineering has several benefits for style transfer and text rewriting tasks:?

1. It is fast and easy to implement.?

Prompt engineering does not require any fine-tuning or additional training of the PLMs, which can be time-consuming and computationally expensive. It only requires designing effective input prompts that can be done in minutes or hours.

2. It is flexible and adaptable.?

Prompt engineering can be applied to various style transfer and text rewriting tasks with different domains, genres, and languages. It can also be customized to suit different preferences, goals, or audiences.

3. It is data-efficient and robust.?

Prompt engineering does not rely on parallel data or explicit labels for style transfer and text rewriting tasks, which are often scarce or noisy. It can leverage the general and specific knowledge of the PLMs that have been pre-trained on large-scale and diverse text corpora.

However, prompt engineering also has some limitations for style transfer and text rewriting tasks:?

1. It is not always reliable or consistent.?

Prompt engineering may produce outputs that are not fluent, coherent, relevant, or accurate. It may also generate outputs that are inconsistent with the desired criteria or contradict each other. This is because PLMs are not perfect and may have biases, errors, or gaps in their knowledge or reasoning.

2. It is not always explainable or controllable.?

Prompt engineering may produce outputs that are difficult to understand or justify. It may also produce outputs that are undesirable or harmful. This is because PLMs are complex and opaque black-box models that may have hidden assumptions or objectives that are not aligned with ours.

3. It is not always scalable or generalizable.?

Prompt engineering may require a lot of trial and error and human expertise to design effective input prompts for different tasks, domains, genres, and languages. It may also require a lot of fine-tuning and adaptation to maintain the quality and diversity of the outputs.

Best Practices and Tips for Prompt Engineering for Style Transfer and Text Rewriting

Prompt engineering is an art and a science that requires creativity and experimentation. However, there are some general best practices and tips that can help to improve the effectiveness and efficiency of prompt engineering for style transfer and text rewriting tasks:?

1. Use existing prompts or templates as a starting point.?

There are many online resources and repositories that provide examples of prompts or templates for various NLP tasks, such as the GPT-3 Playground, the BERT Playground, or the PromptSource. These can be used as a reference or a baseline to design new prompts or templates for style transfer and text rewriting tasks.

2. Use natural language and common sense.?

The input prompts should be written in natural language that is easy to understand and follow by the PLMs. They should also use common sense and logic to provide enough context and guidance for the PLMs to perform the tasks.

3. Use keywords and examples.?

The input prompts should include keywords that indicate the task and the desired criteria, such as “paraphrase”, “summarize”, “simplify”, “positive”, “negative”, “formal”, “informal”, etc. They should also include examples that illustrate the expected output format and quality.?

4. Use instructions and constraints.?

The input prompts should include instructions that specify the requirements and expectations for the output, such as the length, the tone, the style, the content, etc. They should also include constraints that limit the scope or the variability of the output, such as the vocabulary, the grammar, the structure, etc.

5. Use feedback and iteration.?

The output should be evaluated for fluency, coherence, relevance, and accuracy. If the output is not satisfactory, the input prompt should be refined by adding or removing elements, changing the wording or the order, or adjusting the parameters or hyperparameters.

Conclusion

Prompt engineering is a powerful technique that can be used for style transfer and text rewriting tasks. It leverages the knowledge and capabilities of large-scale pre-trained language models to perform various NLP tasks without fine-tuning or additional training. However, prompt engineering also has some challenges and limitations that need to be addressed. Therefore, prompt engineering requires careful design, evaluation, and refinement of input prompts to elicit the desired behavior from the pre-trained language models.?

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